Eliminating Catastrophic Overfitting Via Abnormal Adversarial Examples Regularization
Authors: Runqi Lin, Chaojian Yu, Tongliang Liu
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that our method can effectively eliminate CO and further boost adversarial robustness with negligible additional computational overhead. In this section, we provide a comprehensive evaluation to verify the effectiveness of AAER, including experiment settings (Section 4.1), performance evaluation (Section 4.2), ablation studies (Section 4.3) and time complexity study (Section 4.4). |
| Researcher Affiliation | Academia | Runqi Lin Chaojian Yu Tongliang Liu Sydney AI Centre, The University of Sydney {rlin0511, chyu8051, tongliang.liu}@sydney.edu.au |
| Pseudocode | Yes | Algorithm 1 Abnormal Adversarial Examples Regularization (AAER) |
| Open Source Code | Yes | Our implementation can be found at https://github.com/tmllab/2023_NeurIPS_AAER. |
| Open Datasets | Yes | We evaluate our method on several benchmark datasets, including Cifar-10/100 [22], SVHN [28], Tiny-Image Net [28] and Imagenet-100 [7]. |
| Dataset Splits | No | No explicit statement specifying training/validation/test split percentages or sample counts, or direct citations to specific split methodologies was found for reproducibility. |
| Hardware Specification | Yes | Table 4. CIFAR10 training time on a single NVIDIA RTX 4090 GPU using Preact Res Net-18. |
| Software Dependencies | No | No explicit listing of software dependencies with specific version numbers (e.g., Python 3.8, PyTorch 1.9) was found. |
| Experiment Setup | Yes | In this work, we use the SGD optimizer with a momentum of 0.9, weight decay of 5 Ć 10ā4 and Lā as the threat model. For the learning rate schedule, we use the cyclical learning rate schedule [32] with 30 epochs, which reaches its maximum learning rate (0.2) when half of the epochs (15) are passed. The hyperparameter settings for Cifar-10/100 are summarized in the Table 1. |